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https://github.com/mahseema/aibooks

Curated List of AI, ML and Deep Learning books
https://github.com/mahseema/aibooks

ai-books aibooks awesome-ai-books deep-learning-books ml-books top-ai-books top-ml-books

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Curated List of AI, ML and Deep Learning books

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# AIBooks



A curated list of books on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Transformers. This list is intended for students, educators, researchers, and professionals who are interested in exploring the theoretical foundations, practical applications, and future directions of these technologies.



*Inspired by **[GoBooks](https://github.com/dariubs/GoBooks)***

## Contents



- [Artificial Intelligence & Machine Learning](#artificial-intelligence--machine-learning)

- [Deep Learning](#deep-learning)

- [Transformers and Advanced Topics](#transformers-and-advanced-topics)

- [Practical Guides and Applications](#practical-guides-and-applications)



## Artificial Intelligence & Machine Learning



- **[Artificial Intelligence: A Modern Approach](https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597)** by Stuart Russell and Peter Norvig

  - A comprehensive text that provides an in-depth overview of the entire field of artificial intelligence, including various AI techniques and theories.



- **[Pattern Recognition and Machine Learning](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738)** by Christopher M. Bishop

  - This book offers an introduction to the field of pattern recognition and machine learning, aimed at advanced undergraduates and graduate students.



- **[Machine Learning: A Probabilistic Perspective](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020)** by Kevin P. Murphy

  - Presents machine learning through a probabilistic viewpoint. This book is suitable for students and researchers with a solid mathematics background.



## Deep Learning



- **[Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)** by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

  - A definitive book on deep learning that covers both the theory and practical applications, suitable for beginners and experienced practitioners.



- **[Neural Networks and Deep Learning: A Textbook](https://www.amazon.com/Neural-Networks-Deep-Learning-Textbook/dp/3319944622)** by Charu C. Aggarwal

  - This book provides a detailed examination of neural networks and deep learning, with a focus on cutting-edge techniques and applications.



## Transformers and Advanced Topics



- **[Transformers for Natural Language Processing](https://www.amazon.com/Transformers-Natural-Language-Processing-architectures/dp/1800565798)** by Denis Rothman

  - A guide to understanding and implementing the transformer model, crucial for state-of-the-art NLP applications.



- **["Attention Is All You Need"](https://arxiv.org/abs/1706.03762)** by Ashish Vaswani et al.

  - The seminal paper that introduced the transformer model, essential for anyone looking to understand this revolutionary approach to NLP. (Note: This link goes to the paper on arXiv, as it's not a book available for purchase.)



## Practical Guides and Applications



- **[Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1789955750)** by Sebastian Raschka and Vahid Mirjalili

  - Focuses on implementing practical machine learning projects and techniques using Python.



- **[Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646)** by Aurélien Géron

  - A practical guide for learning machine learning, deep learning, and artificial intelligence using Python libraries.



- **[Building Machine Learning Powered Applications: Going from Idea to Product](https://www.amazon.com/Building-Machine-Learning-Powered-Applications/dp/149204510X)** by Emmanuel Ameisen

  - Provides insights into the process of building machine learning applications, from concept to deployment.



## Contributing



Contributions are welcome! Please read the contribution guidelines before submitting new resources.



## License



[MIT](LICENSE.md)